code: def main(config):
      # Training settings
      parser = argparse.ArgumentParser(
          description="PyTorch MNIST Example"
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=64,
          metavar="N",
          help="input batch size for training (default: 64)",
      )
      parser.add_argument(
          "--test-batch-size",
          type=int,
          default=1000,
          metavar="N",
          help="input batch size for testing (default: 1000)",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=1,
          metavar="N",
          help="number of epochs to train (default: 14)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=1.0,
          metavar="LR",
          help="learning rate (default: 1.0)",
      )
      parser.add_argument(
          "--gamma",
          type=float,
          default=0.7,
          metavar="M",
          help="Learning rate step gamma (default: 0.7)",
      )
      parser.add_argument(
          "--no-cuda",
          action="store_true",
          default=False,
          help="disables CUDA training",
      )
      parser.add_argument(
          "--dry-run",
          action="store_true",
          default=False,
          help="quickly check a single pass",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=1,
          metavar="S",
          help="random seed (default: 1)",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=10,
          metavar="N",
          help="how many batches to wait before logging training status",
      )
      args, _ = parser.parse_known_args()
  
      use_cuda = not args.no_cuda and torch.cuda.is_available()
  
      torch.manual_seed(args.seed)
  
      device = torch.device("cuda" if use_cuda else "cpu")
  
      kwargs = {"batch_size": args.batch_size}
      if use_cuda:
          kwargs.update(
              {"num_workers": 1, "pin_memory": True, "shuffle": True},
          )
  
      transform = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,)),
          ]
      )
      dataset1 = datasets.MNIST(
          "/tmp/torch_mnist_data",
          train=True,
          download=True,
          transform=transform,
      )
      dataset2 = datasets.MNIST(
          "/tmp/torch_mnist_data", train=False, transform=transform
      )
      train_loader = torch.utils.data.DataLoader(dataset1, **kwargs)
      test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
  
      model = Net().to(device)
      optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
      scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
      _, test_acc = test(model, device, test_loader)
  
      compressor = aup.compression.create_compressor(
          model, config, optimizer=optimizer
      )
      model = compressor.compress()
  
      scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
      for epoch in range(1, args.epochs + 1):
          train(args, model, device, train_loader, optimizer, epoch)
          _, test_acc = test(model, device, test_loader)
          scheduler.step()
  
      model = compressor.apply_speedup(
          dummy_input=torch.randn(1, 1, 28, 28).to(device)
      )
      flops, _, _ = compressor.count_flops_params((1, 1, 28, 28))
      flops = int(flops)
      print("test_acc={} flops={}".format(test_acc, flops))
  
      compressor.export_model(
          model_path="mnist_compressed.pth",
          save_best_only=True,
          speedup=True,
          **config,
      )
  
      aup.print_result((test_acc - 1.0) / np.log(flops))
  
file_path: auptimizer/tests/data/task7.py
imports: from __future__ import print_function
  import argparse
  import sys
  import time
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from torch.optim.lr_scheduler import StepLR
  import aup
label_desc: Use image node_id 3 for calling a global function with example usage: main(config) without return types